Digital Twin Technology in Resolving Polycystic Ovary Syndrome and Improving Metabolic Health: A Comprehensive Case...

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Metabolic Disease Management

Background:

• Polycystic ovary syndrome (PCOS) is characterized by heterogeneous clinical manifestations, including anovulation, androgen excess, and insulin resistance.
• A 38-year-old female with PCOS presented with hypertension, obesity, and elevated insulin levels, indicating significant metabolic dysfunction.

Observation:

• The patient utilized a Digital Twin (DT) platform employing AI and IoT for personalized nutrition, predicting glucose responses and suggesting food alternatives via a mobile app.
• Continuous monitoring and AI-driven dietary adjustments were implemented over a 360-day period.

Findings:

• Significant improvements were observed: weight (-12.4%), BMI (-12.4%), waist circumference (-17.0%), blood pressure (-29.17%/-13.98%), fasting insulin (-43.8%), postprandial insulin (-87.0%), and HOMA-IR (-46.2%).
• Further improvements included enhanced estimated glomerular filtration rate (+10.3%), reduced urine microalbumin creatinine ratio (-87.8%), decreased ovarian volume, improved fatty liver infiltration, and substantial reductions in epicardial (-21.8%), pericardial (-69.9%), and visceral fat (-44.4%).

Implications:

• This case demonstrates the efficacy of Digital Twin technology in managing PCOS, leading to comprehensive improvements in metabolic and anthropometric parameters.
• AI-driven personalized interventions show promise for managing chronic conditions like PCOS, paving the way for future research and clinical applications.